The code supports 2D and 3D ordinary and universal kriging. Standard variogram models (linear, power, spherical, gaussian, exponential) are built in, but custom variogram models can also be used. The 2D universal kriging code currently supports regional-linear, point-logarithmic, and external drift terms, while the 3D universal kriging code supports a regional-linear drift term in all three spatial dimensions. Both universal kriging classes also support generic 'specified' and 'functional' drift capabilities. With the 'specified' drift capability, the user may manually specify the values of the drift(s) at each data point and all grid points. With the 'functional' drift capability, the user may provide callable function(s) of the spatial coordinates that define the drift(s). The package includes a module that contains functions that should be useful in working with ASCII grid files (*.asc).
See the documentation at http://pykrige.readthedocs.io/ for more details and examples.
Installation PyKrige requires Python 3.5+ as well as numpy, scipy. It can be installed from PyPi with,
pip install pykrige scikit-learn is an optional dependency needed for parameter tuning and regression kriging. matplotlib is an optional dependency needed for plotting.
If you use conda, PyKrige can be installed from the conda-forge channel with,
conda install -c conda-forge pykrige Features Kriging algorithms OrdinaryKriging: 2D ordinary kriging with estimated mean UniversalKriging: 2D universal kriging providing drift terms OrdinaryKriging3D: 3D ordinary kriging UniversalKriging3D: 3D universal kriging RegressionKriging: An implementation of Regression-Kriging ClassificationKriging: An implementation of Simplicial Indicator Kriging
